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A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems

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  • Gao, Dian-ce
  • Wang, Shengwei
  • Shan, Kui
  • Yan, Chengchu

Abstract

Low delta-T syndrome widely exists in the existing air-conditioning systems and results in increased energy consumption. This paper presents a system-level fault detection and diagnosis method (FDD) to detect and diagnose the low delta-T syndrome resulted from the performance degradation of AHUs system and plate heat exchanger system in a complex HVAC system. Performance indices are introduced to characterize the health status (normal or faulty) of the system. Reference models are developed to generate the benchmarks of the performance indices under fault-free conditions. In order to mitigate the impact of the model fitting uncertainty of the reference models and the measurement uncertainty of the performance indices, adaptive thresholds are adopted using t-statistic approach to identify the health conditions of the performance indices. The proposed method was validated in a dynamic simulation platform built based on a real complex HVAC system studied. The results show that the proposed FDD strategy can successfully detect the low delta-T syndrome, identify the related faults and quantitatively evaluate of the faults severity.

Suggested Citation

  • Gao, Dian-ce & Wang, Shengwei & Shan, Kui & Yan, Chengchu, 2016. "A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems," Applied Energy, Elsevier, vol. 164(C), pages 1028-1038.
    • Handle: RePEc:eee:appene:v:164:y:2016:i:c:p:1028-1038
    DOI: 10.1016/j.apenergy.2015.02.025
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    1. Ma, Zhenjun & Wang, Shengwei, 2011. "Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm," Applied Energy, Elsevier, vol. 88(1), pages 198-211, January.
    2. Ma, Zhenjun & Wang, Shengwei, 2011. "Enhancing the performance of large primary-secondary chilled water systems by using bypass check valve," Energy, Elsevier, vol. 36(1), pages 268-276.
    3. Gao, Dian-ce & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2012. "Diagnosis of the low temperature difference syndrome in the chilled water system of a super high-rise building: A case study," Applied Energy, Elsevier, vol. 98(C), pages 597-606.
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    3. Gao, Dian-ce & Wang, Shengwei & Shan, Kui, 2016. "In-situ implementation and evaluation of an online robust pump speed control strategy for avoiding low delta-T syndrome in complex chilled water systems of high-rise buildings," Applied Energy, Elsevier, vol. 171(C), pages 541-554.
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    11. Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
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    13. Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    14. Wang, Zhanwei & Wang, Zhiwei & He, Suowei & Gu, Xiaowei & Yan, Zeng Feng, 2017. "Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information," Applied Energy, Elsevier, vol. 188(C), pages 200-214.
    15. Yoon, Sungmin & Yu, Yuebin, 2018. "Hidden factors and handling strategies on virtual in-situ sensor calibration in building energy systems: Prior information and cancellation effect," Applied Energy, Elsevier, vol. 212(C), pages 1069-1082.
    16. Shunian Qiu & Zhenhai Li & Delong Wang & Zhengwei Li & Yinying Tao, 2022. "Active Optimization of Chilled Water Pump Running Number: Engineering Practice Validation," Sustainability, MDPI, vol. 15(1), pages 1-12, December.
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